November 23, 2010

Each year I have the pleasure (actually it’s quite fun) of teaching R programming to first year mathematics and statistics students. The vast majority of these students have no experience of programming, yet think they are good with computers because they use facebook!

Debugging students' R scripts

The class has around 100 students, and there are eight practicals. In some of these practicals the students have to submit code. Although the code is “marked” by a script, this only detects if the code is correct. Therefore, I have to go through a lot of R functions by hand and find bugs.

First year the course ran, I had no style guide.

Result: spaghetti R code.

Second year: asked the students to indent their code.

In fact, during practicals I refused to debug in any R code that hadn’t been indented.

Result: nicer looking code and more correct code.

This year I intend to introduce a R style guide based loosely on Google’s and Hadley’s guides.

One point that’s in my guide and not (and shouldn’t be) in the above style guides, is that all functions must have one and only return statement. I tend to follow the single return rule for the majority of my R functions, but do, on occasions, break it. The bible of code styling, Code Complete, recommends that you use returns judiciously.

R Style Guide

This style guide is intended to be very light touch. It’s intended to give students the basis of good programming style, not be a guide for submitting to cran.

File names

File names should end in .R and, of course, be meaningful. Files should be stored in a meaningful directory – not your Desktop!
GOOD: predict_ad_revenue.R
BAD: foo.R

Variable & Function Names

Variable names should be lowercase. Use _ to separate words within a name. Strive for concise but meaningful names (this is not easy!)
GOOD: no_of_rolls
BAD: noOfRolls, free

November 16, 2010

In R, you can use both ‘=’ and ‘<-‘ as assignment operators. So what’s the difference between them and which one should you use?

What’s the difference?

The main difference between the two assignment operators is scope. It’s easiest to see the difference with an example:
##Delete x (if it exists)
> rm(x)
> mean(x=1:10) #[1] 5.5
> x #Error: object 'x' not found

Here x is declared within the function’s scope of the function, so it doesn’t exist in the user workspace. Now, let’s run the same piece of code with using the <- operator:
> mean(x <- 1:10)# [1] 5.5
> x # [1] 1 2 3 4 5 6 7 8 9 10

This time the x variable is declared within the user workspace.

When does the assignment take place?

In the code above, you may be tempted to thing that we “assign 1:10 to x, then calculate the mean.” This would be true for languages such as C, but it isn’t true in R. Consider the following function:
> a <- 1
> f <- function(a) return(TRUE)
> f <- f(a <- a + 1); a
[1] TRUE
[1] 1

Notice that the value of a hasn’t changed! In R, the value of a will only change if we need to evaluate the argument in the function. This can lead to unpredictable behaviour:
> f <- function(a) if(runif(1)>0.5) TRUE else a
> f(a <- a+1);a
[1] 2
> f(a <- a+1);a
[1] TRUE
[1] 2
> f(a <- a+1);a
[1] 3